Comparison of Predictive Modeling Methodologies for Estimating Fuel Use and Emission Rates for Wheel Loaders

Fitriani, Heni and Lewis, Phil Comparison of Predictive Modeling Methodologies for Estimating Fuel Use and Emission Rates for Wheel Loaders. In: International Conference, Construction Research Congress, 2014, Georgia, USA.

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    Abstract

    Heavy duty diesel (HDD) construction equipment consumes significant amounts of fuel and consequently emits substantial quantities of pollutants. The purpose of this paper is to demonstrate three different predictive modeling methodologies for estimating fuel use and emission rates for HDD construction equipment based on real-world data. Engine performance data for five wheel loaders, including manifold absolute pressure (MAP), revolutions per minute (RPM), and intake air temperature (IAT) were used to develop prediction models for fuel use and emission rates of nitrogen oxides (NOx), hydrocarbons (HC), carbon monoxide (CO), carbon dioxide (CO2), and particulate matter (PM). For each wheel loader, predictive models were developed using simple linear regression (SLR), multiple linear regression (MLR), and artificial neural network (ANN). Results indicate that the ANN models accounted for the highest percentage of variability in the data compared to SLR and MLR based on values of the coefficient of determination (R2) for each model. Furthermore, a variable impact analysis was conducted to determine which variables have the most significant impact on fuel use and emission rates for the wheel loaders.

    Item Type: Conference or Workshop Item (Paper)
    Subjects: T Technology > TA Engineering (General). Civil engineering (General)
    Divisions: Faculty of Engineering > Department of Civil Engineering
    Depositing User: Mrs Heni Fitriani
    Date Deposited: 18 May 2016 11:15
    Last Modified: 18 May 2016 11:15
    URI: http://eprints.unsri.ac.id/id/eprint/6440

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